22 research outputs found
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks
Dynamic network slicing has emerged as a promising and fundamental framework
for meeting 5G's diverse use cases. As machine learning (ML) is expected to
play a pivotal role in the efficient control and management of these networks,
in this work we examine the ML-based Quality-of-Transmission (QoT) estimation
problem under the dynamic network slicing context, where each slice has to meet
a different QoT requirement. We examine ML-based QoT frameworks with the aim of
finding QoT model/s that are fine-tuned according to the diverse QoT
requirements. Centralized and distributed frameworks are examined and compared
according to their accuracy and training time. We show that the distributed QoT
models outperform the centralized QoT model, especially as the number of
diverse QoT requirements increases.Comment: accepted for presentation at the IEEE GLOBECOM 201
Distributed collaborative knowledge management for optical network
Network automation has been long time envisioned. In fact, the Telecommunications Management Network (TMN), defined by the International Telecommunication Union (ITU), is a hierarchy of management layers (network element, network, service, and business management), where high-level operational goals propagate from upper to lower layers.
The network management architecture has evolved with the development of the Software Defined Networking (SDN) concept that brings programmability to simplify configuration (it breaks down high-level service abstraction into lower-level device abstractions), orchestrates operation, and automatically reacts to changes or events. Besides, the development and deployment of solutions based on Artificial Intelligence (AI) and Machine Learning (ML) for making decisions (control loop) based on the collected monitoring data enables network automation, which targets at reducing operational costs.
AI/ML approaches usually require large datasets for training purposes, which are difficult to obtain. The lack of data can be compensated with a collective self-learning approach. In this thesis, we go beyond the aforementioned traditional control loop to achieve an efficient knowledge management (KM) process that enhances network intelligence while bringing down complexity.
In this PhD thesis, we propose a general architecture to support KM process based on four main pillars, which enable creating, sharing, assimilating and using knowledge. Next, two alternative strategies based on model inaccuracies and combining model are proposed. To highlight the capacity of KM to adapt to different applications, two use cases are considered to implement KM in a purely centralized and distributed optical network architecture. Along with them, various policies are considered for evaluating KM in data- and model- based strategies. The results target to minimize the amount of data that need to be shared and reduce the convergence error.
We apply KM to multilayer networks and propose the PILOT methodology for modeling connectivity services in a sandbox domain. PILOT uses active probes deployed in Central Offices (COs) to obtain real measurements that are used to tune a simulation scenario reproducing the real deployment with high accuracy. A simulator is eventually used to generate large amounts of realistic synthetic data for ML training and validation.
We apply KM process also to a more complex network system that consists of several domains, where intra-domain controllers assist a broker plane in estimating accurate inter-domain delay. In addition, the broker identifies and corrects intra-domain model inaccuracies, as well as it computes an accurate compound model. Such models can be used for quality of service (QoS) and accurate end-to-end delay estimations.
Finally, we investigate the application on KM in the context of Intent-based Networking (IBN). Knowledge in terms of traffic model and/or traffic perturbation is transferred among agents in a hierarchical architecture. This architecture can support autonomous network operation, like capacity management.La automatizaci贸n de la red se ha concebido desde hace mucho tiempo. De hecho, la red de gesti贸n de telecomunicaciones (TMN), definida por la Uni贸n Internacional de Telecomunicaciones (ITU), es una jerarqu铆a de capas de gesti贸n (elemento de red, red, servicio y gesti贸n de negocio), donde los objetivos operativos de alto nivel se propagan desde las capas superiores a las inferiores. La arquitectura de gesti贸n de red ha evolucionado con el desarrollo del concepto de redes definidas por software (SDN) que brinda capacidad de programaci贸n para simplificar la configuraci贸n (descompone la abstracci贸n de servicios de alto nivel en abstracciones de dispositivos de nivel inferior), organiza la operaci贸n y reacciona autom谩ticamente a los cambios o eventos. Adem谩s, el desarrollo y despliegue de soluciones basadas en inteligencia artificial (IA) y aprendizaje autom谩tico (ML) para la toma de decisiones (bucle de control) en base a los datos de monitorizaci贸n recopilados permite la automatizaci贸n de la red, que tiene como objetivo reducir costes operativos. AI/ML generalmente requieren un gran conjunto de datos para entrenamiento, los cuales son dif铆ciles de obtener. La falta de datos se puede compensar con un enfoque de autoaprendizaje colectivo. En esta tesis, vamos m谩s all谩 del bucle de control tradicional antes mencionado para lograr un proceso eficiente de gesti贸n del conocimiento (KM) que mejora la inteligencia de la red al tiempo que reduce la complejidad. En esta tesis doctoral, proponemos una arquitectura general para apoyar el proceso de KM basada en cuatro pilares principales que permiten crear, compartir, asimilar y utilizar el conocimiento. A continuaci贸n, se proponen dos estrategias alternativas basadas en inexactitudes del modelo y modelo de combinaci贸n. Para resaltar la capacidad de KM para adaptarse a diferentes aplicaciones, se consideran dos casos de uso para implementar KM en una arquitectura de red 贸ptica puramente centralizada y distribuida. Junto a ellos, se consideran diversas pol铆ticas para evaluar KM en estrategias basadas en datos y modelos. Los resultados apuntan a minimizar la cantidad de datos que deben compartirse y reducir el error de convergencia. Aplicamos KM a redes multicapa y proponemos la metodolog铆a PILOT para modelar servicios de conectividad en un entorno aislado. PILOT utiliza sondas activas desplegadas en centrales de telecomunicaci贸n (CO) para obtener medidas reales que se utilizan para ajustar un escenario de simulaci贸n que reproducen un despliegue real con alta precisi贸n. Un simulador se utiliza finalmente para generar grandes cantidades de datos sint茅ticos realistas para el entrenamiento y la validaci贸n de ML. Aplicamos el proceso de KM tambi茅n a un sistema de red m谩s complejo que consta de varios dominios, donde los controladores intra-dominio ayudan a un plano de br贸ker a estimar el retardo entre dominios de forma precisa. Adem谩s, el br贸ker identifica y corrige las inexactitudes de los modelos intra-dominio, as铆 como tambi茅n calcula un modelo compuesto preciso. Estos modelos se pueden utilizar para estimar la calidad de servicio (QoS) y el retardo extremo a extremo de forma precisa. Finalmente, investigamos la aplicaci贸n en KM en el contexto de red basada en intenci贸n (IBN). El conocimiento en t茅rminos de modelo de tr谩fico y/o perturbaci贸n del tr谩fico se transfiere entre agentes en una arquitectura jer谩rquica. Esta arquitectura puede soportar el funcionamiento aut贸nomo de la red, como la gesti贸n de la capacidad.Postprint (published version
Assessment of Cross-train Machine Learning Techniques for QoT-Estimation in agnostic Optical Networks
With the evolution of 5G technology, high definition video, virtual reality, and the internet of things (IoT), the demand for high capacity optical networks has been increasing dramatically. To support the capacity demand, low-margin optical networks engage operator interest. To engross this techno-economic interest, planning tools with higher accuracy and accurate models for the quality of transmission estimation (QoT-E) are needed. However, considering the state-of-the-art optical network鈥檚 heterogeneity, it is challenging to develop such an accurate planning tool and low-margin QoT-E models using the traditional analytical approach. Fortunately, data-driven machine-learning (ML) cognition provides a promising path. This paper reports the use of cross-trained ML-based learning methods to predict the QoT of an un-established lightpath (LP) in an agnostic network based on the retrieved data from already established LPs of an in-service network. This advanced prediction of the QoT of un-established LP in an agnostic network is a key enabler not only for the optimal planning of this network but it also provides the opportunity to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the generalized signal-to-noise ratio (GSNR), which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. The real field data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated synthetic data set, supervised ML techniques such as wide deep neural network, deep neural network, multi-layer perceptron regressor, boasted tree regressor, decision tree regressor, and random forest regressor are applied, demonstrating the GSNR prediction of an un-established LP in an agnostic network with a maximum error of 0.40鈥塪B
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Cross-Layer Platform for Dynamic, Energy-Efficient Optical Networks
The design of the next-generation Internet infrastructure is driven by the need to sustain the massive growth in bandwidth demands. Novel, energy-efficient, optical networking technologies and architectures are required to effectively meet the stringent performance requirements with low cost and ultrahigh energy efficiencies. In this thesis, a cross-layer communications platform is proposed to enable greater intelligence and functionality on the physical layer. Providing the optical layer with advanced networking capabilities will facilitate the dynamic management and optimization of optical switching based on performance monitoring measurements and higher-layer attributes. The cross-layer platform aims to create a new framework for networks to incorporate packet-scale measurement subsystems and techniques for monitoring the health of the optical channel. This will allow for quality-of-service- and energy-aware routing schemes, as well as an enhanced awareness of the optical data signals. This thesis first presents the design and development of an optical packet switching fabric. Leveraging a networking test-bed environment to validate networking hypotheses, advanced switching functionalities are demonstrated, including the support for quality-of-service based routing and packet multicasting. The investigated cross-layering is based on emerging optical technologies, enabling packet protection techniques and packet-rate switching fabric reconfiguration. Coupled with fast performance monitoring, the platform will achieve significant performance gains within the endeavor of all-optical switching. Allowing for a more intelligent, programmable optical layer aims to support greater flexibility with respect to bandwidth allocation and potentially a significant reduction in the network's energy consumption. The ultimate deliverable of this work is a high-performance, cross-layer enabled optical network node. The experimental demonstration of an initial prototype creates a dynamic network element with distributed control plane management, featuring fast packet-rate optical switching capabilities and embedded physical-layer performance monitoring modules. The cross-layer box enables an intelligent traffic delivery system that can dynamically manipulate optical switching on a packet-granular scale. With the goal of achieving advanced multi-layer routing and control algorithms, the network node requires an intelligent co-optimization across all the layers. The proposed cross-layer design should drive optical technologies and architectures in an innovative way, in order to fulfill the void between the design of basic photonic devices and the networking protocols that use them. The performance of the entire network -- from the optical components, to the routing algorithms and user applications -- should be optimized in concert. This contribution to the area of cross-layer network design creates an adaptable optical pipe that is extremely flexible and intelligent aware of both the physical optical signals and higher-layer requirements. The impact of this work will be seen in the realization of dynamic, energy-efficient optical communication links in future networking infrastructures
Optical core networks research in the e-Photon-ONe+ project
This paper reports a summary of the joint research activities on Optical Core Networks within the e-Photon-ONe+ project. It provides a reasonable overview of the topics considered of interest by the European research community and supports the idea of building joint research activities that can leverage on the expertise of different research groups. 漏 2009 IEEE
Machine learning for optical fiber communication systems: An introduction and overview
Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is
extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt
both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a
disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and
dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine
learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we
embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and
graph-based machine learning for the networking layer
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions
Machine Learning for Multi-Layer Open and Disaggregated Optical Networks
L'abstract 猫 presente nell'allegato / the abstract is in the attachmen